Computing devices have numerous applications that convey text information or other data between different networked users. For example, text messaging or chat messaging applications convey text data among networked devices in an unstructured manner. Such text data may have identifiers including sender and/or recipient information as well as date and/or time stamps. However, such applications typically do not organize the text data in a manner that identifies relevant portions of text data based on the substance of the text data itself and/or different topics or categories to which the text data may be attributed. As such, solutions are needed that help to organize, categorize and convey portions of text data in a meaningful and useful way to computing device users.
Current solutions for identifying information of interest may involve manual searching through a large volume of text data or other data. This often involves a substantial amount of time spent by a user scrolling or otherwise searching through a long list of chat recipients and/or chat messages. Some chat messaging or text messaging applications include tools for implementing a keyword search through the text included in such applications. However, even keyword searching tools lack an ability to aggregate information from different messages and/or recipients in a meaningfully related manner. In particular, a need exists within such applications to identify related text and correlate it to particular events or activities.